Abstract
Sleep is an important part of our life that significantly influences our health and well-being. The monitoring of sleep can provide data based on which sleep quality could be improved. This paper presents a system for heart rate detection during sleep. The data is collected from sensors underneath the test subjects. Though the data contains noise, it needs to be filtered to remove it. Due to the low strength of the signals, they need to be amplified after filtering. At some points of the signal, particular heartbeats may not be tracked by sensors due to the failure of a sensor or other reasons, which should be considered. The heart rate is detected in intervals of 15 s. A tool is implemented that detects the heart rate and visualizes it. The preprocessing of the data is performed with several filters: a highpass filter, a band-reject filter, a lowpass filter, and a motion detector. After the preprocessing of the data, the quality of the signal is significantly increased, and detection is possible.
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References
Penzel, T.: Sleep Laboratory (2006). https://doi.org/10.1002/9780471740360.ebs1099
Irwin, M.R.: Why sleep is important for health: a psychoneuroimmunology perspective. Ann. Rev. Psychol. 66, 143–172 (2015). https://doi.org/10.1146/annurev-psych-010213-115205
Chokroverty, S.: Sleep. Indian J. Med. Res. (2017). https://doi.org/10.1016/S0030-6665(05)70123-7
Pavlova, M.K., Latreille, V.: Sleep disorders. Am. J. Med. 132, 292–299 (2019). https://doi.org/10.1016/j.amjmed.2018.09.021
Gaiduk, M., et al.: Estimation of sleep stages analyzing respiratory and movement signals. IEEE J. Biomed. Health Inform. 26(2), 505–514 (2022). https://doi.org/10.1109/JBHI.2021.3099295
Jiapu, P., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985). https://doi.org/10.1109/TBME.1985.325532
Afonso, V.X., Tompkins, W.J., Nguyen, T.Q., Luo, S.: ECG beat detection using filter banks. IEEE Trans. Biomed. Eng. (1999). https://doi.org/10.1109/10.740882
Phan, D., Siong, L.Y., Pathirana, P. N., Seneviratne, A.: Smartwatch: performance evaluation for long-term heart rate monitoring. In: 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB), pp. 144–147 (2015)
Inan, O.T., et al.: Ballistocardiography and seismocardiography: a review of recent advances. IEEE J. Biomed. Health Inform. (2015). https://doi.org/10.1109/JBHI.2014.2361732
Pollock, P.: Ballistocardiography: a clinical review. Can. Med. Assoc. J. 76(9), 778–83 (1957)
Starr, I., et al.: Studies on the estimation of cardiac output in man, and of abnormalities in cardiac function, from the heart’s recoil and the blood’s impacts; the ballistocardiogram. Am. J. Physiol. 127, 1–28 (1939)
Alvarado-Serrano, C., Luna-Lozano, P.S., Pallàs-Areny, R.: An algorithm for beat-to-beat heart rate detection from the BCG based on the continuous spline wavelet transform. Biomed. Sig. Process. Control 27, 96–102 (2016). https://doi.org/10.1016/j.bspc.2016.02.002
Daubechies, I.: Where do wavelets come from? a personal point of view. Proc. IEEE 84, 510–513 (1996). https://doi.org/10.1109/5.488696
Acharya, U.R., Fujita, H., Sudarshan, V.K., et al.: Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals. Neural Comput. Appl. (2016). https://doi.org/10.1007/s00521-016-2612-1
Nazari, G., Bobos, P., MacDermid, J. C., Sinden, K.E., Richardson, J., Tang, A.: Psychometric properties of the Zephyr bioharness device: a systematic review. BMC Sports Sci. Med. Rehabil. 10(6) (2018). https://doi.org/10.1186/s13102-018-0094-4
Alivar, A., Carlson, C., Suliman, A., et al.: Motion detection in bed-based ballistocardiogram to quantify sleep quality. In: GLOBECOM 2017—2017 IEEE Global Communications Conference, pp. 1-6. https://doi.org/10.1109/GLOCOM.2017.8255014
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Rätzer, S., Gaiduk, M., Seepold, R. (2022). Heart Rate Detection Using a Non-obtrusive Ballistocardiography Signal. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_35
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DOI: https://doi.org/10.1007/978-981-19-3444-5_35
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